Learning from Censored and Truncated Data in Practice

Author(s)
Stefanou, Patroklos N.
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Advisor
Daskalakis, Constantinos
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In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/
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Abstract
An experimental study of the methods and algorithms developed to learn from truncated data. In my work, I provide a theoretical framework used to learn from missing data, and then show results from the package that I have developed to alleviate such biases.
Date issued
2022-05
URI
https://hdl.handle.net/1721.1/144548
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
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